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  <meta name="description" content="文章浏览阅读8k次，点赞83次，收藏211次。Preprocess：预处理Classify：分类Cluster：聚类Associate：关联Select attributes：选择属性Visualize：可视化open file：从文件中打开一组实例open URL：从URL中打开一组实例open DB：从数据库中打开一组实例generate：生成人工数据undo：撤销对数据集最后更改edit：在查看器中打开当前数据集进行编辑save：将工作关系保存到文件中属性ALL：全选None：全不选。_weka安装教程"> 
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         <h1 class="title-article" id="articleContentId">〖数据挖掘〗weka3.8.6的安装与使用</h1> 
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         <p><strong>目录</strong></p> 
         <p style="margin-left:0px;"><a href="#%E8%83%8C%E6%99%AF">背景</a></p> 
         <p style="margin-left:0px;"><a href="#%E4%B8%80%E3%80%81%E5%AE%89%E8%A3%85">一、安装</a></p> 
         <p style="margin-left:0px;"><a href="#%E4%BA%8C%E3%80%81%E4%BD%BF%E7%94%A8explorer">二、使用explorer</a></p> 
         <p style="margin-left:40px;"><a href="#1.%20%E4%BB%8B%E7%BB%8D">1. 介绍</a></p> 
         <p style="margin-left:40px;"><a href="#2.%E6%89%93%E5%BC%80%E8%87%AA%E5%B8%A6%E7%9A%84%E6%95%B0%E6%8D%AE%E9%9B%86%28Preprocess%29">2.打开自带的数据集(Preprocess)</a></p> 
         <p style="margin-left:80px;"><a href="#1.%E6%89%93%E5%BC%80%E6%AD%A5%E9%AA%A4">1.打开步骤</a></p> 
         <p style="margin-left:80px;"><a href="#2.%E6%9F%A5%E7%9C%8B%E5%B1%9E%E6%80%A7">2.查看属性和数据编辑</a></p> 
         <p style="margin-left:40px;"><a href="#3.classify">3.classify</a></p> 
         <p style="margin-left:40px;"><a href="#4.Cluster">4.Cluster</a></p> 
         <p style="margin-left:40px;"><a href="#5.Associate">5.Associate</a></p> 
         <p style="margin-left:40px;"><a href="#6.Select%20attributes">6.Select attributes</a></p> 
         <p style="margin-left:40px;"><a href="#7.Visualize">7.Visualize</a></p> 
         <p style="margin-left:0px;"><a href="#%E5%BE%85%E8%A1%A5%E5%85%85">待补充</a></p> 
         <hr> 
         <h2>背景</h2> 
         <blockquote> 
          <p>Weka的全名是怀卡托智能分析环境（Waikato Environment for Knowledge Analysis），是一款免费<br> 的，非商业化软件，与之对应的是SPSS公司商业数据挖掘产品--Clementine ；weka是基于JAVA环境下开源的<br> 机器学习（machine learning）以及数据挖掘（data mining）软件。Weka的主要开发者来自新西兰的<a href="https://baike.baidu.com/item/%E6%80%80%E5%8D%A1%E6%89%98%E5%A4%A7%E5%AD%A6/7082322?fromModule=lemma_inlink" title="怀卡托大学" rel="nofollow">怀卡托大学</a>（The University of Waikato）。WEKA作为一个公开的数据挖掘工作平台，集合了大量能承担数据挖掘任务的机器学习算法，包括对数据进行预处理，分类，回归、聚类、关联规则以及在新的交互式界面上的可视化。</p> 
         </blockquote> 
         <h2>一、安装</h2> 
         <blockquote> 
          <p>安装简单，一路next。</p> 
         </blockquote> 
         <p>1.打开weka3.8.6的安装包</p> 
         <p><img alt="01a951b74d564fc6a163646f64f61793.png" src="https://i-blog.csdnimg.cn/blog_migrate/5fb4511cb50bd34f74f22c434dec99ef.png"></p> 
         <p>2.点击 next</p> 
         <p><img alt="fc075071a2ca4ce781b3893ec5e31aa2.png" src="https://i-blog.csdnimg.cn/blog_migrate/7983708b22633628e28c453880311c25.png"></p> 
         <p>&nbsp;3.点击I Agree</p> 
         <p><img alt="1a1770dee2484f2a89fb0a87121f1440.png" src="https://i-blog.csdnimg.cn/blog_migrate/c8dd2195c8d476304402e0fee1b5c40c.png"></p> 
         <p>4.点击next</p> 
         <p><img alt="f413b17809164c7eb582fc20fc46a10a.png" src="https://i-blog.csdnimg.cn/blog_migrate/7b41043d2b92f37b6dc429b905c8bcc4.png"></p> 
         <p>5.选择安装路径如：D:\weka-3-8-6</p> 
         <p><img alt="0eb3e6ea3ebc4e5189bba8655f82f0ac.png" src="https://i-blog.csdnimg.cn/blog_migrate/8b9f0f2f0ca18d770c9dd687e25bd14b.png"></p> 
         <p>6.点击install</p> 
         <p><img alt="9aa430e8e47045eea074f1b943fd0740.png" src="https://i-blog.csdnimg.cn/blog_migrate/b361b6fbe2654b89c1f06b2837efc472.png"></p> 
         <p>等待后，finish。下面是安装成功后打开的初始界面<img alt="d257cfe5582f4121bb138ff161730403.png" src="https://i-blog.csdnimg.cn/blog_migrate/5d87139d6964009f420bc1a33363a304.png"></p> 
         <h2>二、使用explorer</h2> 
         <p><img alt="bb3bb2f14f8a4b479f02ad2b4844fbe9.png" src="https://i-blog.csdnimg.cn/blog_migrate/74c95fe802750884ff5fc0272368f445.png"></p> 
         <h3>1. 介绍</h3> 
         <p><img alt="5832686aa24c45e99253b48d03ed201b.png" src="https://i-blog.csdnimg.cn/blog_migrate/84e25096d6cc3af3b48c794f9bf9ee29.png"></p> 
         <p>Preprocess：预处理<br> Classify：分类<br> Cluster：聚类<br> Associate：关联<br> Select attributes：选择属性<br> Visualize：可视化</p> 
         <p><img alt="e3c730d43d4245a0887bcac5a08e5a73.png" src="https://i-blog.csdnimg.cn/blog_migrate/69328968c0f3b891707a919f084b3b0e.png"></p> 
         <ul>
          <li>open file：从文件中打开一组实例</li>
          <li>open URL：从URL中打开一组实例</li>
          <li>open DB：从数据库中打开一组实例</li>
          <li>generate：生成人工数据</li>
          <li>undo：撤销对数据集最后更改</li>
          <li>edit：在查看器中打开当前数据集进行编辑</li>
          <li>save：将工作关系保存到文件中</li>
         </ul> 
         <p>&nbsp;<img alt="2fa52ca9246f4acba8585f27e8750598.png" src="https://i-blog.csdnimg.cn/blog_migrate/f10c8073468a383920cda4813ee0e32b.png"></p> 
         <p>属性</p> 
         <ul>
          <li>ALL：全选</li>
          <li>None：全不选</li>
          <li>invert：取反</li>
          <li>pattern：输入perl正则表达式</li>
         </ul> 
         <p><img alt="2b52ccf01a1544df95502cc4755df3b3.png" src="https://i-blog.csdnimg.cn/blog_migrate/d7c72e67d037f13672403c72ca0a3b69.png"></p> 
         <p>该位置展示具体属性都有什么</p> 
         <ul>
          <li>remove：选择属性后，点击删除，可用undo撤回。</li>
         </ul> 
         <p><img alt="aae1d5c2df6247b9ba9e5fa181f8cc67.png" src="https://i-blog.csdnimg.cn/blog_migrate/4ec00acf7982ce1ca580ba891798ba42.png"></p> 
         <p>当前的关系</p> 
         <ul>
          <li>关系:投票&nbsp; 属性:17</li>
          <li>实例:435&nbsp;&nbsp; 权重和:435</li>
         </ul> 
         <h3>2.打开自带的数据集(Preprocess)</h3> 
         <h4>1.打开步骤</h4> 
         <p>选择openfile</p> 
         <p><img alt="80a83d63817b43eeb63dff1fa8b61e50.png" src="https://i-blog.csdnimg.cn/blog_migrate/2d0db6e99207c705e11f570ebde3b96b.png"></p> 
         <p>选择data文件</p> 
         <p>&nbsp;<img alt="1f006d3c282f43fca20a650fc4bcadb0.png" src="https://i-blog.csdnimg.cn/blog_migrate/e963022d5841e57047ae2b046e6b7722.png"></p> 
         <p>这里我们可以看到一些weka自带的数据集</p> 
         <p><img alt="468d19189e1c4a7b8ecbe141894ecaac.png" src="https://i-blog.csdnimg.cn/blog_migrate/861ae43e106494560b7770cb15c4c1a8.png"></p> 
         <p>选择其中一个数据集打开，我选择的是vote.arff</p> 
         <p><img alt="05d29e668a5746d68c33cd51023b290d.png" src="https://i-blog.csdnimg.cn/blog_migrate/91cfedeb8a33ba4906b70c389512167b.png"></p> 
         <h4>2.查看属性和数据编辑</h4> 
         <p>2.1查看属性</p> 
         <p>选择属性，可以下拉查看并选择</p> 
         <p><img alt="b326139b40dd4401b96c9db33f3dfb95.png" src="https://i-blog.csdnimg.cn/blog_migrate/19e2b7f4776f65171d68cb6ae092d8b2.png"></p> 
         <p>&nbsp;<img alt="411d7e5d8a154f05a4c7b4edc4be3ee5.png" src="https://i-blog.csdnimg.cn/blog_migrate/fd33c1bcca00a7857dc568911de33b99.png"></p> 
         <p>Visualize All：查看所有属性直方图</p> 
         <p><img alt="b467e84288424be5946c50301e209527.png" src="https://i-blog.csdnimg.cn/blog_migrate/79793c7d775479e1772c5bb3597fe12f.png"></p> 
         <p>2.2数据编辑</p> 
         <p><img alt="001043e1292641cb88faec51ded3d323.png" src="https://i-blog.csdnimg.cn/blog_migrate/be9070fd9fc1dd09dd02db979c45686d.png"></p> 
         <p>&nbsp;点击Edit，弹出viewer，在这里面可以查看并编辑数据。</p> 
         <p><img alt="3dafa6212b744971944cc9d04d1e8f58.png" src="https://i-blog.csdnimg.cn/blog_migrate/51354468a7edb3d318bef007da7c0772.png"></p> 
         <h3>3.classify</h3> 
         <blockquote> 
          <p>分类是指根据事物的某些共同特征，将它们分成若干组或类别，以达到简化、归纳和管理的目的。在机器学习领域中，分类是指根据已有数据集中的特征和标签信息，训练出一个算法模型，用于预测新样本所属的类别。分类问题是机器学习中最常见的问题类型，其应用广泛，如垃圾邮件识别、图像分类、医学诊断等等。机器学习中一些常见的分类算法包括决策树、支持向量机、朴素贝叶斯、神经网络等。</p> 
         </blockquote> 
         <p><img alt="d5237fb1bdaa45ceb563ce22bfe3484f.png" src="https://i-blog.csdnimg.cn/blog_migrate/6f034f8a4c25574c21253b76b57b93bb.png"></p> 
         <p><img alt="e66e72f21e6244c3b6b5ddd827b5331b.png" src="https://i-blog.csdnimg.cn/blog_migrate/9d7915cecf082d4b116d454e61ac2119.png"></p> 
         <p>测试选项&nbsp;</p> 
         <ul>
          <li>Use training set：使用训练集</li>
          <li>Supplied test set：提供测试集</li>
          <li>Cross-validation：交叉验证</li>
          <li>Percentage split：按比例分配</li>
          <li>start：点击即用</li>
         </ul> 
         <h3>4.Cluster</h3> 
         <blockquote> 
          <p>聚类是指根据一组数据的相似性，将它们分为若干个组或簇，使得同一个簇内的元素彼此相似，不同簇之间的元素差异较大。聚类是无监督学习领域中的重要问题，与分类问题不同，聚类问题中没有预先定义好的标签信息，需要通过算法自动挖掘数据的内在结构和规律。聚类算法可以应用于数据挖掘、图像分割、社交网络分析等领域，是机器学习中的重要研究方向。常见的聚类算法有K-means、层次聚类、DBSCAN、OPTICS等。</p> 
         </blockquote> 
         <p><img alt="b837b1cfd4b74607901c8a87081670fa.png" src="https://i-blog.csdnimg.cn/blog_migrate/c748462ef226b88d1857884448768601.png"></p> 
         <p>集群模式【Cluster mode】</p> 
         <ul>
          <li>Use training set：使用训练集</li>
          <li>Supplied test set：提供测试集</li>
          <li>Percentage split：比例分割</li>
          <li>Classes to clusters evalation：类到聚类的评估</li>
         </ul> 
         <h3>5.Associate</h3> 
         <p><img alt="6faacb8663174c688e803a37c5b12138.png" src="https://i-blog.csdnimg.cn/blog_migrate/a033e52841e8838f6f84b4fa2b381de6.png"></p> 
         <h3>6.Select attributes</h3> 
         <p><img alt="8246a87ad7fd40f8a2ed8018931cb38b.png" src="https://i-blog.csdnimg.cn/blog_migrate/078af5e483f4c089cfee16a992ebe500.png"></p> 
         <p>属性选择模式</p> 
         <ul>
          <li> <p>Use full training set：使用完整的训练集</p> </li>
          <li>Cross-validation：交叉验证</li>
         </ul> 
         <h3>7.Visualize</h3> 
         <p><img alt="fb79175aea5c4f5fb3cf9a61ae0cd6b2.png" src="https://i-blog.csdnimg.cn/blog_migrate/c4c1493be258b02499c52323d2d729ac.png"></p> 
         <ul>
          <li>update:更新</li>
          <li>select attribute：选择属性</li>
          <li>subsample：子样品</li>
         </ul> 
         <p></p> 
         <p><strong>软件获取方式：公众号回复：weka</strong></p> 
         <p></p> 
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       <p>内容来源：csdn.net</p> 
       <p>作者昵称：码银</p> 
       <p>原文链接：https://hw-mayin.blog.csdn.net/article/details/130553092</p> 
       <p>作者主页：https://hw-mayin.blog.csdn.net</p> 
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